Mobile brain-based device for use in a real world environment

ABSTRACT

A mobile brain-based device BBD includes a mobile base equipped with sensors and effectors (Neurally Organized Mobile Adaptive Device or NOMAD), which is guided by a simulated nervous system that is an analogue of cortical and sub-cortical areas of the brain required for visual processing, decision-making, reward, and motor responses. These simulated cortical and sub-cortical areas are reentrantly connected and each area contains neuronal units representing both the mean activity level and the relative timing of the activity of groups of neurons. The brain-based device BBD learns to discriminate among multiple objects with shared visual features, and associated “target” objects with innately preferred auditory cues. Globally distributed neuronal circuits that correspond to distinct objects in the visual field of NOMAD 10 are activated. These circuits, which are constrained by a reentrant neuroanatomy and modulated by behavior and synaptic plasticity, result in successful discrimination of objects. The brain-based device BBD is moveable, in a rich real-world environment involving continual changes in the size and location of visual stimuli due to self-generated or autonomous, movement, and shows that reentrant connectivity and dynamic synchronization provide an effective mechanism for binding the features of visual objects so as to reorganize object features such as color, shape and motion while distinguishing distinct objects in the environment.

PRIORITY CLAIM

This application is a continuation of U.S. patent application Ser. No.11/105,019, filed Apr. 13, 2005, entitled “Mobile Brain-Based Device forUse in a Real World Environment,” by Anil K. Seth et al., which claimspriority under 35 U.S.C. 119(e) to U.S. Provisional Patent ApplicationNo. 60/562,376, filed Apr. 15, 2004, entitled “Mobile Brain-Based Devicefor Use in a Real World Environment,” by Anil K. Seth et al., whichapplications are incorporated herein by reference.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH AND DEVELOPMENT

This invention was made with Government support under N00014-03-1-0980awarded by the Office of Naval Research. The United States Governmenthas certain rights in the invention.

COPYRIGHT NOTICE

A portion of the disclosure of this patent document contains materialwhich is subject to copyright protection. The copyright owner has noobjection to the facsimile reproduction by anyone of the patent documentor the patent disclosure, as it appears in the Patent and TrademarkOffice patent file or records, but otherwise reserves all copyrightrights whatsoever.

FIELD OF THE INVENTION

The present invention relates to the field of brain-based devices havingsimulated nervous systems for guiding the behavior of the devices in areal world environment.

BACKGROUND OF THE INVENTION

A brain-based device is a device that has a sensing system for receivinginformation, effectors that enable the device to move about, and asimulated nervous system which controls movement of the effectors inresponse to input from the sensing system to guide the behavior of thebrain-based device in a real-world environment. The sensing system mayinclude video and audio sensors which receive image and audioinformation from the real-world environment in which the device moves.The simulated nervous system may be implemented as a computer-basedsystem which receives and processes the image and auditory informationinput to the brain-based device and outputs commands to the effectors tocontrol the behavior of the device in the environment.

The simulated nervous system, while implemented in a computer-basedsystem, emulates the human brain rather than a programmed computer whichtypically follows a set of precise executable instructions or whichperforms computations. That is, the brain is not a computer and followsneurobiological rather than computational principles in itsconstruction. The brain has special features or organization andfunctions that are not believed to be consistent with the idea that itfollows such a set of precise instructions or that it computes in themanner of a programmed computer. A comparison of the signals that abrain receives with those of a computer shows a number of features thatare special to the brain. For example, the real world is not presentedto the brain like a data storage medium storing an unambiguous series ofsignals that are presented to a programmed computer. Nonetheless, thebrain enables humans (and animals) to sense their environment,categorize patterns out of a multitude of variable signals, and initiatemovement. The ability of the nervous system to carry out perceptualcategorization of different signals for sight, sound, etc. and dividethem into coherent classes without a prearranged code is special andunmatched by present day computers, whether based on artificialintelligence (AI) principles or neural network constructions.

The visual system of the brain contains a variety of cortical regionswhich are specialized to different visual features. For example, oneregion responds to the color of an object, another separate regionresponds to the object's shape, while yet another region responds to anymotion of the object. The brain will enable a human to see anddistinguish in a scene, for example, a red airplane from a gray cloudboth moving across a background of blue sky. Yet, no single region ofthe brain has superordinate control over the separate regions respondingto color, shape and movement that coordinate color, shape and movementso that we see and distinguish a single object (e.g. the airplane) anddistinguish it from other objects in the scene (e.g. the cloud and thesky).

The fact that there is no such single superordinate control region inthe brain poses what is known as the “binding problem.” How do thesefunctionally separated regions of the brain coordinate their activitiesin order to associate features belonging to individual objects anddistinguish among different objects? It is this ability of the brain toso associate and distinguish different objects that enables us to moveabout in our real-world environment. A mobile brain-based device havinga simulated nervous system that can control the behavior of the devicein the rich environment of the real world therefore would have manyadvantages and uses.

Mechanisms proposed for solving the “binding problem” generally fallinto one of two classes: (i) binding through the influence of “higher”attentional mechanisms of the brain, and (ii) selective synchronizationof the “firing” of dynamically formed groups of neurons in the brain. In(i), the belief is that the brain through its parietal or frontalregions, “binds” objects by means of an executive mechanism, forexample, a spotlight of attention that would combine visual featuresappearing at a single location in space, e.g. the red airplane or graycloud against the background of a blue sky. In (ii), the belief is thatthe brain “binds” objects in an automatic, dynamic, and pre-attentiveprocess through groups of neurons that become linked by selectivesynchronization of the firing of the neurons. These synchronizedneuronal groups form within the brain into global patterns of activity,or circuits, corresponding to perceptual categories. This enables us tosee, for example, a red, flying airplane as a single object distinctfrom other objects such as a gray, moving cloud.

Computer-based computational models of visual binding, as well asphysical, mobile brain-based devices having a simulated nervous system,are known, Yet, neither provides emergent circuits in the computer modelor in the simulated nervous system of the physical brain-based devicethat contribute to providing a device with a rich and variable behaviorin the real-world environment, especially in environments that requirepreferential behavior towards one object among many in a scene. Forexample, it would be desirable to have a mobile brain-based device moveabout in an environment and have preferential behavior toward one objectamong many in a scene so as to be able to obtain images of that objectvia an on-board camera and to select that object via on-board grippers.

One prior computational computer model simulated the nervous system byrepresenting nine neural areas analogous to nine cortical areas of thevisual system of the brain. It also simulated “reward” and motor systemsof the nervous system. The model had “reentrant connections” or circuitsbetween the nine different cortical areas, which are connections thatallow the cortical areas to interact with each other. This computationalmodel showed the capabilities of reentrant circuits to result inbinding; the computer model, however, had several limitations. Thestimuli into the modeled nervous system came from a limited predefinedset of simulated object shapes and these were of uniform scale, contraryto what is found in a real-world environment. Furthermore, the resultingmodeled behavior did not emerge in a rich and noisy environmentexperienced by behaving organisms in the real world. A more detaileddescription of this computational model is given in the paper entitled“Reentry and the Problem of Integrating Multiple Cortical Areas:Simulation of Dynamic Integration in the Visual System”, by Tononi andEdelman, Cerebral Cortex, July/August 1992.

A prior physical, mobile brain-based device having a simulated nervoussystem does explore its environment and through this experience learnsto develop adaptive behaviors. Such a prior mobile brain-based device isguided by the simulated nervous system which is implemented on acomputer system. The simulation of the nervous system was based on theanatomy and physiology of vertebrate nervous systems, but as with anysimulated nervous system, with many fewer neurons and a simplerarchitecture than is found in the brain. For this physical, mobilebrain-based device, the nervous system was made up of six major neuralareas analogous to the cortical and subcortical brain regions. These sixmajor areas included: an auditory system, a visual system, a tastesystem, a motor system capable of triggering behavior, a visual trackingsystem, and a value system. A detailed description of this mobilebrain-based device is given in the paper entitled “Machine Psychology:Autonomous Behavior, Perceptual Categorization and Conditioning in aBrain-based Device” by Krichmar and Edelman, Cerebral Cortex, August2002. While this brain-based device does operate in a real-worldenvironment, it does not implement, among many other things, reentrantconnections, thereby limiting its ability to engage in visually guidedbehavior and in object discrimination in a real-world environment.

SUMMARY OF THE INVENTION

The present invention is a physical, mobile brain-based device (“BBD”)having a simulated nervous system for guiding the device in a richexploratory and selective behavior in a real-world environment. Thesimulated nervous system of this device contains simulated neural areasanalogous to the ventral stream of a brain's visual system, known asneural areas V1 V2, V4 and IT that influence visual tracking (neuralarea C), and neural areas having a value system (area S). These neuralareas have reentrant connections within and between each other, whichgive rise to biases in motor activity, which in turn evoke behavioralresponses in the mobile device enabling visual object discrimination ina scene.

Each neural area is comprised of many neuronal units. And, to representthe relative timing of neuronal activity, each neuronal unit in eachneural area is described by a firing rate variable and a phase variable,where similar phases reflect synchronous firing. The binding problem,therefore, in the present invention is resolved based on principles ofreentrant connectivity and synchronous neuronal firing.

The physical, mobile device of the present invention, as it is movingand interacting in the real world in a conditioning or training stage,learns what objects are in its environment, i.e. objects are not givento it as predefined data in a simulation. That is, the brain-baseddevice of the present invention learns, in a given environment, what isa particular object, such as a green diamond, what is a floor, what is awall, etc. Moreover, this learning through movement and interaction inthe environment results in the brain-based device having invariantobject recognition. This means that once it learns what, for example, agreen diamond is as an object during a training stage, it will recognizethat object when in a testing stage as the device moves about itsreal-world environment whether the object is across a room from thedevice, directly in front of the device, off to the left of the device,off to the right of the device, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a pictorial view of a physical, mobile brain-based device.

FIG. 2 is a schematic of the regional and functional neuroanatomy of thesimulated nervous system of the brain-based device of FIG. 1.

FIGS. 3A-3E are views of a simple network of three neuronal units usedto explain the neuroanatomy of the present invention shown in FIG. 2.

FIGS. 4A-4E illustrate graphically activity vs. phase of a neuronalunit.

FIGS. 5A-5B illustrates schematically and photographically,respectively, an experimental set-up of a real-world environment inwhich the mobile brain-based device of FIGS. 1 and 2 behaves.

FIGS. 6A-6B are used to explain, respectively, the training and testingprotocol of the brain-based device of FIGS. 1 and 2.

FIGS. 7A-7B are graphs illustrating the behavior of the brain-baseddevice of FIGS. 1 and 2 following conditioning.

FIG. 8 is a snapshot of the neuronal unit activity of the brain-baseddevice of FIGS. 1 and 2 during a behavioral experiment.

FIGS. 9A-9B show phase responses with and without reentry connections,respectively, of the brain-based device of FIGS. 1 and 2 followingconditioning.

FIGS. 10A-10C are used to explain phase correlations among neural areasfor the brain-based device of FIGS. 1 and 2 conditioned to prefer ordiscriminate a red diamond as a single target object.

FIGS. 11A-11B are illustrations used to explain the response of theneural value area S to target objects in different real-world positionsand at different scales.

FIGS. 12A-12B show neural activity during conditioning or training ofthe brain-based device of FIGS. 1 and 2 for neural areas S, IT and C.

FIG. 13 is an exemplary illustration of a system in accordance withvarious embodiments of the invention.

FIG. 14 is a flow diagram illustration of neural simulatorinitialization.

FIG. 15 is a flow diagram illustration of the master component inaccordance with various embodiments of the invention.

FIG. 16 is a flow diagram illustration of a neural simulator inaccordance with various embodiments of the invention.

DETAILED DESCRIPTION

Aspects of the invention are illustrated by way of example and not byway of limitation in the figures of the accompanying drawings in whichlike references indicate similar elements. It should be noted thatreferences to “an”, “one” and “various” embodiments in this disclosureare not necessarily to the same embodiment, and such references mean atleast one. In the following description, numerous specific details areset forth to provide a thorough description of the invention. However,it will be apparent to one skilled in the art that the invention may bepracticed without these specific details. In other instances, well-knownfeatures have not been described in detail so as not to obscure theinvention.

FIG. 1 is a pictorial view of a brain-based device (BBD) of the presentinvention which includes physically instantiated mobile NeurallyOrganized Mobile Adaptive Device (NOMAD) 10 which can explore itsenvironment and develop adaptive behavior while experiencing it. Thebrain-based device BBD also includes a simulated nervous system 12 (FIG.2) for guiding NOMAD 10 in its real-world environment. In oneembodiment, the simulated nervous system 12, as will be furtherdescribed, can run on a cluster of computer workstations (see FIG. 13)remote from NOMAD 10. In this embodiment, NOMAD 10 and the computerworkstations communicate with one another via wireless communication,thereby enabling untethered exploration of NOMAD 10.

NOMAD 10 develops or adapts its behavior by learning about theenvironment using the simulated nervous system 12. As NOMAD 10 movesautonomously in its environment, it will approach and view multipleobjects that share visual features, e.g. same color, and have distinctvisual features such as shape, e.g. red square vs. red triangle. NOMAD10 can become conditioned through the learning experience to prefer onetarget object, e.g. the red diamond, over multiple distracters ornon-target objects such as the red square and a green diamond of a scenein its vision. NOMAD 10 learns this preference behaviorally while movingin its environment by orienting itself towards the target object inresponse to an audible tone.

NOMAD 10 has a CCD camera 16 for vision and microphones 18, 20 on eitherside of camera 16, which can provide visual and auditory sensory inputto simulated nervous system 12, as well as effectors or wheels 22 formovement. It also has an infrared (IR) sensor 24 at the front of NOMAD10 for obstacle avoidance by sensing differences in reflectivity of thesurface on which it moves, and for triggering reflexive turns of NOMAD10 in its environment. NOMAD 10 also contains a radio modem to transmitstatus, IR sensor information, and auditory information to the computerworkstation carrying out the neural simulation via simulated nervoussystem 12 and to receive motor commands from the simulated nervoussystem 12 to control effectors 22. Video output from camera 16 can besent to the computer workstations via RF transmission. All behavioralactivity of NOMAD 10, other than the IR reflexive turns, is evoked bysignals received from simulated nervous system 12.

FIG. 2 is a schematic diagram of the regional and functionalneuroanatomy of simulated nervous system 12 which guides the behavior ofNOMAD 10 in its environment. Simulated nervous system 12 is modeled onthe anatomy and physiology of the mammalian nervous system but, as canbe appreciated, with far fewer neurons and a much less complexarchitecture. Simulated nervous system 12 includes a number of neuralareas labeled according to the analogous cortical and subcorticalregions of the human brain. Thus, FIG. 2 shows respective neural areaslabeled as V1, V2, V4, IT, S, A-left, Mic-left, A-right, Mic-right andC, whose activity controls the tracking of NOMAD 10. Each neural areaV1, V2, etc. contains different types of neuronal units, each of whichrepresents a local population of neurons. Each ellipse shown in FIG. 2(except “Tracking”) denotes a different neural area, with each such areahaving many neuronal units. To distinguish modeled or simulated neuralareas from corresponding regions in the mammalian nervous system, thesimulated areas are indicated in italics, e.g. IT.

The neuroanatomy of FIG. 2 also shows schematically various projectionsP throughout the simulated nervous system 12. A projection can be“feedforward” from one neural area to another, such as the projection P1from neural area V1 to neural area V2. A projection P may also be“reentrant” between neural areas such as the reentrant projection P2from neural area IT to neural area V4 and reentrant projection P4 fromneural area V4 to neural area V2. Reentrant projections P marked with an“X” were removed from the simulated nervous system 12 during “lesion”experiments as will be further described. Furthermore, projections Phave properties as indicated by the legend in FIG. 2, which are (1)“excitatory voltage independent”, (2) “excitatory voltage dependent”,(3) “plastic”, (4) “inhibitory,” and (5) “value dependent.”

The simulated nervous system 12 shown in FIG. 2 is comprised of foursystems: a visual system, a tracking system, an auditory system and avalue system.

FIG. 2—Visual System. Neural Areas V1, V2, V4, IT

The visual system is modeled on the primate occipitotemporal or ventralcortical pathway and includes neural areas V1→V2→V4→IT in which neuronsin successive areas have progressively larger receptive fields until, ininferotemporal cortex, receptive fields cover nearly the entire visualfield. Visual images from the CCD camera 16 of NOMAD 10 are filtered forcolor and edges and the filtered output directly influences neuralactivity in area V1. V1 is divided into subregions (not shown) eachhaving neuronal units that respond preferentially to green (V1-green),red (V1-red), horizontal line segments (V1-horizontal), vertical linesegments (V1-vertical), 45-degree lines (V1-diagonal-right), and135-degree lines (V1-diagonal-left). This visual system provides acomputationally tractable foundation for analyzing higher-levelinteractions within the visual system and between the visual system andother cortical areas.

Subregions of neural area V1 project topographically to correspondingsubregions of neural area V2. The receptive fields of neuronal units inarea V2 are narrow and correspond closely to pixels from the image ofCCD camera 16. Neural area V2 has both excitatory and inhibitoryreentrant connections within and among its subregions. Each V2 subregionprojects to a corresponding V4 subregion topographically but broadly, sothat neural area V4's receptive fields are larger than those of neuralarea V2. Neural area V4 subregions project back to the correspondingneural area V2 subregions with non-topographic reentrant connections.The reentrant connectivity within and among subregions of area V4 issimilar to that in area V2. V4 projects in turn non-topographically toneural area IT so that each neuronal unit in neural area IT receivesinput from three V4 neuronal units randomly chosen from three differentV4 subregions. Thus, while neuronal units in IT respond to a combinationof visual inputs, the level of synaptic input into a given IT neuronalunit is fairly uniform; this prevents the activity of individual ITneuronal units from dominating the overall activity patterns. ITneuronal units project to other IT neuronal units through plasticconnections, and back to neural area V4 through non-topographicreentrant connections.

FIG. 2—Tracking System—Neural Area C

The tracking system allows NOMAD 10 to orient towards auditory andvisual stimuli. The activity of neural area C (analogous to the superiorcolliculus) dictates where NOMAD 10 directs its camera gaze. Tracking inNOMAD 10 is achieved by signals to wheels 22 based on the vectorsummation of the activity of the neuronal units in area C. Each neuronalunit in area C has a receptive field which matches its preferreddirection, and the area has a topographic arrangement such that ifactivity is predominately on the left side of area C, signals to NOMAD10's wheels 22 are issued that evoke a turn towards the left. Theauditory neural areas (A-left and A-right) have strong excitatoryprojections to the respective ipsilateral sides of area C causing NOMAD10 to orient towards a sound source. Neural area V4 projectstopographically to area C, its activity causing NOMAD 10 to center itsgaze on a visual object (e.g. a red triangle). Both neural areas IT andthe value system S project to area C, and plastic connections in thepathways IT→C and IT→S facilitate target selection by creating a bias inactivity, reflecting salient perceptual categories (see Value System,below). As will be described below, prior to a conditioning or trainingstage, because of a lack of bias, NOMAD 10 will direct its gazepredominately between two objects in its environment (e.g. a redtriangle and a red square). After learning to prefer a visual object(e.g. a red triangle), changes in the strengths of the plasticconnections result in greater activity in those parts of area Ccorresponding to the preferred object's position.

FIG. 2—Auditory System—Neural Areas Mic-Left, Mic-Right, A-Left, A-Right

This system converts inputs from microphones 16,18 into simulatedneuronal unit activity. Neural areas Mic-left and Mic-right arerespectively activated whenever the corresponding microphones 16, 18detect a sound of sufficient amplitude within a specified frequencyrange. Mic-left/Mic-right project to neuronal units in areasA-left/A-right. Sound from one side results in activity on theipsilateral side of the auditory system, which in turn produces activityon the ipsilateral side of area C causing orientation of NOMAD 10towards the sound source.

FIG. 2—Value System—Neural Area S

Activity in the simulated value system signals the occurrence of salientsensory events and this activity contributes to the modulation ofconnection strengths in pathways IT→S and IT→C. Initially, in thelearning stage to be described below, neural area S is activated bysounds detected by auditory system (see A-left→S and A-right→S ofnervous system 12). Activity in area S is analogous to that of ascendingneuromodulatory systems in that it is triggered by salient events,influences large regions of the simulated nervous system (describedbelow in the section Synaptic Plasticity), and persists for severalcycles. In addition, due to its projection to the tracking area C, areaS has a direct influence on the behavior of NOMAD 10 in its real-worldenvironment.

Details of the values of certain parameters of the neuronal units withinthe respective neural areas V1, V2, etc. shown in FIG. 2 are given inTable 1, described below. Details of the anatomical projections andconnection types of neuronal units of the neural areas V1, V2, etc. aregiven in Table 2, described below. As is known, a neuronal unit can beconsidered pre- or post- a synapse (see “A Universe of Consciousness”,by Edelman and Tononi, Basic Books, 2000, FIG. 4.3, for a description ofa synapse and pre- and post-synaptic neurons.) The simulated nervoussystem 12 used in the experiments described below contains 28 neuralareas V1, V2, etc., 53,450 neuronal units, and approximately 1.7 millionsynaptic connections.

Neuronal Units—Generally

In one embodiment, a neuronal unit within a neural area V1, V2, etc. ofthe simulated nervous system 12 is simulated by a mean firing ratemodel. The state of each neuronal unit is determined by both a meanfiring rate variable (σ) and a phase variable (P). The mean firing ratevariable of each neuronal unit corresponds to the average activity orfiring rate of a group of roughly 100 neurons during a time period ofapproximately 100 milliseconds. The phase variable, which specifies therelative timing of firing activity, provides temporal specificitywithout incurring the computational costs associated with modeling ofthe spiking activity of individual neurons in real-time (see NeuronalUnit Activity and Phase, below).

Synaptic Connections—Generally

In one embodiment, synaptic connections between neuronal units, bothwithin a given neural area, e.g. V1 or C, and between neural areas, e.g.V2→V4 or C→V4, are set to be either voltage-independent orvoltage-dependent, either phase-independent or phase-dependent, andeither plastic or non-plastic, as indicated by the legend in FIG. 2.Voltage-independent connections provide synaptic input to apost-synaptic neuron regardless of the post-synaptic state of theneuron. Voltage-dependent connections represent the contribution ofreceptor types (e.g. NMDA receptors) that require post-synapticdepolarization to be activated. In other words, a pre-synaptic neuronwill send a signal along its axon through a synapse to a post-synapticneuron. The post-synaptic neuron receives this signal and integrates itwith other signals being received from other pre-synaptic neurons.

A voltage independent connection is such that if a pre-synaptic neuronis firing at a high rate, then a post-synaptic neuron connected to itvia the synapse will fire at a high rate.

A voltage dependent connection is different. If the post-synaptic neuronis already firing at some rate when it receives a pre-synaptic inputsignal, then the voltage-dependent connection will cause thepost-synaptic neuron to fire more. Since the post-synaptic neuron isactive, i.e. already firing, this neuron is at some threshold level.Therefore, the pre-synaptic connection will modulate the post-synapticneuron to fire even more. The voltage-dependent connection, no matterhow active the pre-synaptic neuron is, would have no affect on thepost-synaptic neuron if the latter were not above the threshold value.That is, the post-synaptic neuron has to have some given threshold ofactivity to be responsive or modulated by a voltage-dependent synapticconnection.

In the simulated nervous system 12 of FIG. 2, all within-neural areaexcitatory connections and all between-neural area reentrant excitatoryconnections are voltage-dependent (see FIG. 2 and Table 2). Thesevoltage-dependent connections, as described above, play a modulatoryrole in neuronal dynamics.

Phase-dependent synaptic connections influence both the activity, i.e.firing rate, and the phase of post-synaptic neuronal units, whereasphase-independent synaptic connections influence only their activity.All synaptic pathways in the simulated nervous system 12 arephase-dependent except those involved in motor output (see Table 2:A-left/A-right→C, C

C) or sensory input (see Table 2: Mic-left/Mic-right→A-left/A-right,A-left⇄A-right, V1→V2), since signals at these interfaces are defined bymagnitude only. Plastic connections are either value-independent orvalue-dependent, as described below.

Neuronal Synchrony in a Simple Network Model

FIGS. 3A-3E illustrate how reentrant connections among neuronal unitscan lead to neuronal synchrony in a mean firing rate model with a phaseparameter as indicated above and, thereby, help solve the “bindingproblem” described above. FIG. 3A illustrates a simple network modelconsisting of three neuronal units (n1-n3). Units n1 and n2 receive,respectively, steady phase-independent input (solid input arrows) andproject via respective voltage-independent connections to the thirdneuronal unit n3 (solid input arrows). Units n1 and n2 project to eachother and unit n3 projects back to both units n1 and n2, via reentrantvoltage-dependent connections (shown by dotted arrows).

FIG. 3B is a graph of phase vs. cycle and shows that in this simplifiedmodel all neuronal units n1-n3 become synchronized within 10 simulationcycles. By contrast, if reentrant connections are removed (the dottedarrows in FIG. 3A being “lesioned”) so that only feedforward projectionsremain (the remaining solid arrows in FIG. 3A), synchrony is notachieved, as shown by the graph of FIG. 3C. While for clarity FIGS.3B-3C show only the first 15 simulation cycles, these cycles arerepresentative of network behavior in the real world of NOMAD 10 overlong durations such as 10,000 cycles.

FIGS. 3D and 3E show the probability distributions from whichpostsynaptic phases are chosen for each neuronal unit. With reentrantconnections intact (FIG. 3D), distributions for all neurons n1-n3 becomepeaked at the same phase. With reentrant connections absent, i.e. noreentry (“lesioned” networks, FIG. 3E), the probability distributionsfor neuronal units n1 and n2 remain flat due to their phase-independentinputs, and the distribution for unit n3 varies randomly over time.

To explore whether the synaptic property of connection strength isimportant for network behavior, the above analysis was repeated severaltimes using different random seeds, and a network was compared in whichall weights were set to a mean value (1.45). After 10,000 cycles,qualitatively identical results occurred to those shown in FIGS. 3B-3E.To explore the effect of the property of connection plasticity, theabove was repeated for networks in which value-independent plasticitywas enabled for the feedforward projections for neuronal units n1→n3 andn2→n3 (solid arrows). As before, networks were analyzed with randomlyselected weights as well as networks with all weights set to a meanvalue (1.45). In both of these cases, synchrony in intact reentrynetworks and no synchrony in lesioned networks occurred. Also, sincepre- and post-synaptic neuronal units were correlated in activity andphase, plastic connections in the intact networks increased in strengthby nearly 100% over 1000 cycles. In lesioned networks, however, becausepre-and post-synaptic units were not in phase with each other and theseconnections were depressed to about 10% of their initial values over thesame duration.

The above indicate the importance of reentry connections to the “bindingproblem.” That is, the results from this reduced model of FIG. 3A showthat the presence of reentrant connections can facilitate synchronousactivity among neural areas, that this synchrony does not depend onspecific or differential connection strengths, and that the absence ofreentry is not compensated by synaptic plasticity. The simulated nervoussystem 12 of the present invention has three major differences from thisreduced model of FIG. 3A. System 12 has a large-scale reentrantneuroanatomy based on the vertebrate visual cortex as shownschematically in FIG. 2 and detailed in Table 1 and Table 2 below; itinvolves value-dependent and value-independent synaptic plasticity; andit allows NOMAD 10 to behave autonomously in a real-world environment.

Neuronal Unit Activity and Phase—Details

In various embodiments, the mean firing rate (s) of each neuronal unitranges continuously from 0 (quiescent) to 1 (maximal firing). The phase(p) is divided into 32 discrete bins representing the relative timing ofactivity of the neuronal units by an angle ranging from 0 to 2π. Thestate of a neuronal unit is updated as a function of its current stateand contributions from voltage-independent, voltage-dependent, andphase-independent synaptic connectors. The voltage-independent input cto neuronal unit i from a unit j is:

A _(ij) ^(VI)(t)=_(c) _(ij) _(s) _(j) (t),

where s_(j)(t) is the activity of unit j, and c_(ij) is the connectionstrength from unit j to unit i. The voltage-independent post-synapticinfluence on unit i is calculated by convolving this value into acosine-tuning curve over all phases:

${{POST}_{i}^{VI} = {\sum\limits_{l = 1}^{M}\; {\sum\limits_{j = 1}^{N_{l}}\; \left( {{A_{ij}^{VI}(t)}{\sum\limits_{k = 1}^{32}\; \left( \frac{{\cos \left( {\left( {2{\pi/32}} \right)\left( {k - {p_{j}(t)}} \right)} \right)} + 1}{2} \right)^{tw}}} \right)}}},$

where M is the number of different anatomically defined connection types(see Table 2); N_(i) is the number of connections of type M projectingto neuronal unit i; p_(j)(t) is the phase of neuronal unit j at time t;and tw is the tuning width, which, in one embodiment, may be set to 10so that the width of the tuning curve is relatively sharp (˜5 phasebins).

The voltage-dependent input to neuronal unit i from unit j is:

${{A_{ij}^{VD}(t)} = {{\Phi \left( {{POST}_{i}^{VI}\left( {p_{j}(t)} \right)} \right)}c_{ij}{s_{j}(t)}}},{{{where}{\Phi (x)}} = \left\{ {\begin{matrix}{0;} & {x < \sigma_{i}^{vdep}} \\{x;} & {otherwise}\end{matrix},} \right.}$

where σ_(i) ^(vdep) is a threshold for the post-synaptic activity belowwhich voltage-dependent connections have no effect (see Table 1).

The voltage-dependent post-synaptic influence on unit i is given by:

${POST}_{i}^{VD} = {\sum\limits_{l = 1}^{M}\; {\sum\limits_{j = 1}^{N_{l}}\; {\left( {{A_{ij}^{VD}(t)}{\sum\limits_{k = 1}^{32}\; \left( \frac{{\cos \left( {\left( {2{\pi/32}} \right)\left( {k - {p_{j}(t)}} \right)} \right)} + 1}{2} \right)^{tw}}} \right).}}}$

The phase-independent activation into unit i from unit j is:

A _(ij) ^(PI)(t)=_(c) _(ij) s(t)

The phase-independent post-synaptic influence on unit i is a uniformdistribution based on all the phase-independent inputs divided by thenumber of phase bins (32).

${{POST}_{i}^{PI}(p)} = {\sum\limits_{l = 1}^{M}\; {\sum\limits_{j = 1}^{N_{l}}\left( \frac{A_{ij}^{PI}(t)}{32} \right)}}$

A new phase, p_(i)(t+1), and activity, s_(i)(t+1) are chosen based on adistribution created by linearly summing the post-synaptic influences onneuronal unit i (see FIGS. 4A-4E):

${POST}_{i} = {{\sum\limits_{j = 1}^{N_{VI}}{POST}_{j}^{VI}} + {\sum\limits_{k = 1}^{N_{VD}}\; {POST}_{k}^{VD}} + {\sum\limits_{l = 1}^{N_{PI}}\; {POST}_{l}^{PI}}}$

The phase threshold, σ_(i) ^(phase), of the neuronal unit is subtractedfrom the distribution POST_(i) and a new phase, p_(i)(t+1), iscalculated with a probability proportional to the resulting distribution(FIG. 4E). If the resulting distribution has an area less than zero(i.e. no inputs are above the phase threshold), a new phase, p_(i)(t+1),is chosen at random. The new activity for the neuronal unit is theactivity level at the newly chosen phase, which is then subjected to thefollowing activation function:

si^((t + 1)) = φ(tanh (g_(i)(POST_(i)(p_(i)(t + 1)) + ω_(Si)(t)))), where${\varphi (x)} = \left\{ {\begin{matrix}{0;} & {x < \sigma_{i}^{fire}} \\{x;} & {otherwise}\end{matrix},} \right.$

where ω determines the persistence of unit activity from one cycle tothe next, g_(i) is a scaling factor, and σ_(i) ^(fire) a unit specificfiring threshold.

Specific parameter values for neuronal units are given in Table 1, andsynaptic connections are specified in Table 2.

TABLE 1 Neuronal unit parameters. Area Size σ-fire σ-phase σ-vdep ω g V1(6) 60 × 80 — — — — — V2 (6) 30 × 40 0.10 0.45 0.05 0.30  1.0* V4 (6) 15× 20 0.20 0.45 0.10 0.50  1.0* C 15 × 20 0.10 0.10 0.10 0.50 1.0 IT 30 ×30 0.20 0.20 0.10 0.75 1.0 S 4 × 4 0.10 0.00 0.00 0.15 1.0 Mic-right 1 ×1 — — — — — Mic-left 1 × 1 — — — — — A-left 4 × 4 0.00 0.00 0.10 0.501.0 A-right 4 × 4 0.00 0.00 0.10 0.50 1.0

As shown in Table 1, area V1 is an input neural area and its activity isset based on the image of camera 16 of FIG. 1. Neural areas V1, V2 andV4 have six sub-areas each with neuronal units selective for color (e.g.red and green), and line orientation (e.g. 0, 45, 90 and 135 degrees).Neural areas Mic-left and Mic-right are input neural areas and theiractivity is set based on inputs from microphones 18, 20 (FIG. 1).

Table 1 also indicates the number of neuronal units in each neural areaor sub-area (“Size” column). Neuronal units in each area apart fromneural areas V1, Mic-left and Mic-right have a specific firing threshold(σ-fire), a phase threshold (σ-phase), a threshold above whichvoltage-dependent connections can have an effect (σ-vdep), a persistenceparameter (ω), and a scaling factor (g). Asterisks in Table 1 markvalues that are set to 1.0 for simulated nervous system 12 (FIG. 2) withlesioned reentrant connections (see Table 2).

TABLE 2 Properties of anatomical projections and connection types.Projection Arbor P c_(ij)(0) type η θ₁ θ₂ k1 k2 V1→V2 □ 0 × 0 1.00 1, 2PI 0.00 0 0 0.00 0.00 V2→V2(intra) □ 3 × 3 0.75 0.45, 0.85 VD 0.00 0 00.00 0.00 V2→V2(inter) (X) □ 2 × 2 0.40  0.5, 0.65 VD 0.00 0 0 0.00 0.00V2→V2(intra) ⊖ 18, 25 0.10 −0.05, −0.1  VI 0.00 0 0 0.00 0.00V2→V2(inter) □ 2 × 2 0.05 −0.05, −0.1  VI 0.00 0 0 0.00 0.00 V2→V4 □ 3 ×3 0.40  0.1, 0.12 VI 0.00 0 0 0.00 0.00 V4→V2 (X) □ 1 × 1 0.10 0.25,0.5  VD 0.00 0 0 0.00 0.00 V4→V4(inter) (X) □ 2 × 2 0.40 1.75, 2.75 VD0.00 0 0 0.00 0.00 V4→V4(intra) ⊖ 10, 15 0.10 −0.15, −0.25 VI 0.00 0 00.00 0.00 V4→V4(inter) ⊖ 10, 15 0.10 −0.15, −0.25 VI 0.00 0 0 0.00 0.00V4→V4(inter) □ 2 × 2 0.03 −0.15, −0.25 VI 0.00 0 0 0.00 0.00 V4→C □ 3 ×3 1.00  0.002, 0.0025 VI 0.00 0 0 0.00 0.00 V4→IT special —  0.1, 0.15VI 0.00 0 0 0.00 0.00 IT→V4 (X) non-topo 0.01 0.05, 0.07 VD 0.00 0 00.00 0.00 IT→IT non-topo 0.10 0.14, 0.15 VD 0.10 0 0.866 0.90 0.45 IT→C# non-topo 0.10 0.2, 0.2 VD 1.00 0 0.707 0.45 0.65 IT→S # non-topo 1.000.0005, 0.001  VI 0.10 0 0.707 0.45 0.45 C→V4 (X) non-topo 0.01 0.05,0.07 VD 0.00 0 0 0.00 0.00 C→C ⊖ 6, 12 0.50 −0.05, −0.15 PI 0.00 0 00.00 0.00 C→Mleft non-topo 1.00 35, 35 VD 0.00 0 0 0.00 0.00 C→Mrightnon-topo 1.00 35, 35 VD 0.00 0 0 0.00 0.00 S→C non-topo 0.50 0.5, 05  VD0.00 0 0 0.00 0.00 S→S non-topo 0.50 0.7, 0.8 VD 0.00 0 0 0.00 0.00A-left→C left-only 1.00 0.5, 0.5 VD 0.00 0 0 0.00 0.00 A-right→Cright-only 1.00 0.5, 0.5 VD 0.00 0 0 0.00 0.00 A-left→C right-only 1.00−0.15, −0.15 PI 0.00 0 0 0.00 0.00 A-right→C left-only 1.00 −0.15, −0.15PI 0.00 0 0 0.00 0.00 A-left→S non-topo 1.00 35, 35 VD 0.00 0 0 0.000.00 A-right→S non-topo 1.00 35, 35 VD 0.00 0 0 0.00 0.00 A-left

A-right non-topo 1.00 −1, −1 PI 0.00 0 0 0.00 0.00 A-left

A-right non-topo 1.00 −0.5, −0.5 VD 0.00 0 0 0.00 0.00 Mic-left,Mic-right→A-left, A-right non-topo 1.00 5, 5 PI 0.00 0 0 0.00 0.00

Table 2 shows properties of anatomical projections and connection typesof simulated nervous system 12. A pre-synaptic neuronal unit connects toa post-synaptic neuronal unit with a given probability (P) and givenprojection shape (Arbor). This arborization shape can be rectangular “□”with a height and width (h×w), doughnut shaped “Θ” with the shapeconstrained by an inner and outer radius (r1, r2), left-only(right-only) with the pre-synaptic neuronal unit only projecting to theleft (right) side of the post-synaptic area, or non-topographical(“non-topo”) where any pairs of pre-synaptic and post-synaptic neuronalunits have a given probability of being connected. The initialconnection strengths, C_(ij)(O), are set randomly within the range givenby a minimum and maximum value (min, max). A negative value forC_(ij)(O), indicates inhibitory connections. Connections marked with“intra” denote those within a visual sub-area and connections markedwith “inter” denote those between visual sub-areas. Inhibitory “inter”projections connect visual sub-areas responding to shape only or tocolor only (e.g. V4-red

V4-green, V4-horizontal

V4-vertical), excitatory “inter” projections connect shape sub-areas tocolor sub-areas (e.g. V4-red

V4-vertical). Projections marked # are value-dependent. A connectiontype can be phase-independent/voltage-independent (PI),phase-dependent/voltage-independent (VI), orphase-dependent/voltage-dependent (VD). Non-zero values for η, θ₁, θ₂,k₁, and k₂ signify plastic connections. The connection from V4 to IT wasspecial in that a given neuronal unit in area IT was connected to threeneuronal units randomly chosen from three different V4 sub-areas.Projections marked with an “X” were removed during lesion experiments.

In this model of a neuronal unit, post-synaptic phase tends to becorrelated with the phase of the most strongly active pre-synapticinputs. This neuronal unit model facilitates the emergence ofsynchronously active neuronal circuits in both a simple network (seeFIG. 3A above, Neuronal Synchrony in a Simple Network Model) and in thefull simulated nervous system (FIG. 2), where such emergence involvesadditional constraints imposed by reentrant connectivity, plasticity,and behavior.

Synaptic Plasticity

Synaptic strengths are subject to modification according to a synapticrule that depends on the phase and activities of the pre- andpost-synaptic neuronal units. Plastic synaptic connections are eithervalue-independent (see IT→IT in FIG. 2) or value-dependent (see IT→S,IT→C in FIG. 2). Both of these rules are based on a modified BCMlearning rule in which thresholds defining the regions of depression andpotentiation are a function of the phase difference between thepre-synaptic and post-synaptic neuronal units (see FIG. 2, inset). Thegraphical inset shown in FIG. 2 shows a form of the known BCM rule inwhich synaptic change (ΔC_(ij)) is a function of the phase differencebetween post-and pre-synaptic neuronal units (ΔP) and two thresholds (θ₁and θ₂).

Synapses between neuronal units with strongly correlated firing phasesare potentiated and synapses between neuronal units with weaklycorrelated phases are depressed; the magnitude of change is determinedas well by pre- and post-synaptic activities. This learning rule issimilar to a spike-time dependent plasticity rule applied to jitteredspike trains where the region of potentiation has a high peak and a thintail, and the region of depression has a comparatively small peak andfat tail.

Value-independent synaptic changes in c_(ij) are given by:

Δ_(c) _(ij) (t+1)=η_(s) _(i) (t)_(s) _(j) (t)BCM(Δp),

where s_(i)(t) and s_(j)(t) are activities of post- and pre-synapticunits, respectively, η is a fixed learning rate, and

${{\Delta \; p} = \frac{{\cos \left( {\left( {2{\pi/32}} \right)\left( {{p_{i}(t)} - {p_{j}(t)}} \right)} \right)} + 1}{2}},$

where p_(i)(t) and p_(j)(t) are the phases of post- and pre-synapticunits (0.0≦Δp≦1.0). A value of Δp near 1.0 indicates that pre-andpost-synaptic units have similar phases, a value of Δp near 0.0indicates that pre- and post-synaptic units are out of phase. Thefunction BCM is implemented as a piecewise linear function, taking Δp asinput, that is defined by two thresholds (θ₁, θ₂, in radians), twoinclinations (k₁, k₂) and a saturation parameter ρ (ρ=6 throughout):

${{BCM}\left( {\Delta \; p} \right)} = \left\{ {\begin{matrix}{0;} & {{\Delta \; p} < \theta_{1}} \\{{k_{1}\left( {\theta_{1} - {\Delta \; p}} \right)};} & {\theta_{1} \leq {\Delta \; p} < {\left( {\theta_{1} + \theta_{2}} \right)/2}} \\{{k_{1}\left( {{\Delta \; p} - \theta_{2}} \right)};} & {{\left( {\theta_{1} + \theta_{2}} \right)/2} \leq {\Delta \; p} < \theta_{2}} \\{{k_{2}{{\tanh \left( {\rho \left( {{\Delta \; p} - \theta_{2}} \right)} \right)}/\rho}};} & {otherwise}\end{matrix},} \right.$

Specific parameter settings for fine-scale synaptic confections aregiven in Table 2.

The rule for value-dependent synaptic plasticity differs from thevalue-independent rule in that an additional term, based on the activityand phase of the value system (neural areas), modulates the synapticstrength changes. Synaptic connections terminating on neuronal unitsthat are in phase with the value system are potentiated, and connectionsterminating on units out of phase with the value system are depressed.

The synaptic change for value-dependent synaptic plasticity is given by:

Δ_(c) _(ij) (t+1)=η_(s) _(i) (t)_(s) _(j) (t)BCM(Δp)V(t)BCM _(v)(Δp_(v)),

where V(t) is the mean activity level in the value areas S at time t.Note that the BCM_(v) function is slightly different than the BCMfunction above in that it uses the phase difference between area S andthe post-synaptic neuronal unit as input

$\left( {{{\Delta \; p_{v}} = \frac{{\cos \left( {\left( {2{\pi/32}} \right)\left( {{p_{V}(t)} - {p_{i}(t)}} \right)} \right)} + 1}{2}},} \right.$

where p_(v)(t) is the mean phase in area S. When both BCM and BCM_(v)return a negative number, BCM_(v) is set to 1 to ensure that thesynaptic connection is not potentiated when both the pre-synapticneuronal unit and value system (neural areas) are out of phase with thepost-synaptic neuronal unit.

Simulated Cycle Computation

During each simulation cycle of simulated nervous system 12, sensoryinput is processed, the states of all neuronal units are computed, theconnection strengths of all plastic connections are determined, andmotor output is generated. In experiments described below, execution ofeach simulated cycle required approximately 100 milliseconds of realtime.

Experimental Conditions

FIG. 5A shows a diagram of the environment of NOMAD 10. The environmentconsisted of an enclosed area with black walls. Various pairs of shapesfrom a set consisting of a green diamond, a green square, a red diamond,and a red square were hung on two opposite walls. The floor was coveredwith opaque black plastic panels, and contained a boundary made ofreflective construction paper. When this boundary was detected by theinfrared (IR) detector attached to the front of NOMAD 12 and facingtoward the floor, NOMAD 10 made one of two reflexive movements: (i) ifan object was in its visual field, it backed up, stopped and then turnedroughly 180 degrees, (ii) if there was no object in its visual field,NOMAD 10 turned roughly 90 degrees, thus orienting away from wallswithout visual stimuli. Near the boundary of walls containing visualshapes, infrared emitters (IR) on one side of the room were paired withIR sensors containing speakers on the other side (as shown in FIG. 5A),to create an IR beam. If the movement of NOMAD 10 broke either IR beam,a tone was emitted by the speakers. Detection of the tone by NOMAD 10elicited an orientation movement towards the source of the sound via thesimulated nervous system 12.

Experimental Protocol—FIGS. 6A-6B

FIGS. 6A and 6B illustrate the experimental set-up for NOMAD 10. NOMAD10 views objects on two of the walls of an area, which is about “90 by66”. Experiments were divided into two stages, training and testing, asshown in FIGS. 6A and 6B, respectively. During both stages the activityand phase responses of all neuronal units of neural areas V1, V2, etc.were recorded for analysis.

During training as shown in FIG. 6A, NOMAD 10 explored its enclosure for10,000 simulation cycles corresponding to roughly 24 approaches to thepairs of various objects shown. Responses to sounds emitted by thespeaker (auditory cues) caused NOMAD 10 to orient toward the target,which in this example is the red diamond. The distracters, which wereexchanged before every sixth approach to ensure that left-rightorientation of NOMAD 10 did not confound target relation, are a greendiamond and a red square. For testing, as shown in FIG. 6B, the speakerswere turned off and NOMAD 10 was allowed to explore the environment for15,000 simulation cycles. While the target object was continuouslypresent for these 15,000 cycles, the distracters were changed every5,000 cycles.

Training Stage Details—FIG. 6A

In the training stage shown in FIG. 6A, NOMAD 10 autonomously exploredits enclosure for 10,000 simulation cycles, corresponding to 15-20minutes of real time and approximately 24 approaches to the variouspairs of visual shapes which were a red diamond and red square (on theleft of FIG. 6A) and a red diamond and a green diamond (on the right ofFIG. 6A). Thus, each pair contained a “target” shape (red diamond) and a“distracter” shape (green diamond on a red square). Distracters weredeliberately designed to share attributes with the target, for example,when the red diamond was the target, a red diamond/red square pair washung on one wall (shown on left side of FIG. 6A), and a reddiamond/green diamond pair was hung on the other wall (shown on rightside of FIG. 6A). The red diamond on either side of the room was closestto the speakers in both cases, as illustrated. To ensure that theleft-right orientation of shapes in the target-distracter pair (e.g.red-square on the left, green-diamond on the right) did not confoundtarget selection, the side of the distracters were exchanged every sixthviewing of a pair. During the training stage, responses to the speakerscaused NOMAD 10 to orient towards the target.

Testing Stage Details—FIG. 6B

During testing, as shown in FIG. 6B, the speakers were turned off(therefore not shown), and NOMAD 10 was allowed to autonomously exploreits enclosure for 15,000 simulation cycles. The first 10,000 cyclesinvolved encounters with the same target and distracters present duringthe training stage of FIG. 6A. The final 5,000 cycles involvedencounters with the target and the single shape of the set of fourshapes (left and right) that did not share any features with the target(e.g. a pair consisting of a red diamond as target and a green square asdistracter).

Training and testing were repeated with three different “subjects” ofthe brain-based device BBD using each of the four shapes as a target (atotal of 12 training and testing sessions). Each BBD “subject” had thesame physical device of NOMAD 10, but each possessed a unique simulatednervous system 24. This variability among “subjects” was a consequenceof random initialization in both the microscopic details of connectivitybetween individual neuronal units and the initial connection strengthsbetween those neuronal units. The overall connectivity among neuronalunits remained similar among different “subjects”, however, inasmuch asthat connectivity was constrained by the synaptic pathways, arborizationpatterns, and ranges of initial connection strengths (see FIG. 2 andTable 2 for specifics).

Target Tracking Behavior—Generally—FIGS. 7A-7B

The discrimination performance of each “subject” of the brain-baseddevice BBD was assessed by how well that “subject” tracked toward targetobjects in the absence of auditory cues following conditioning ortraining, as shown in FIGS. 7A-7B. This was calculated as the fractionof time for which the target was centered in NOMAD 10's visual field viacamera 16 during each approach to a pair of visual objects shown in FIG.6B. Three separate “subjects” were conditioned to prefer one of fourtarget shapes or objects, i.e. red diamond (rd), red square (rs), greensquare (gs) and green diamond (d). Activity in neural area V2 was usedto assess the percentage of time for which the visual field of NOMAD 10via its camera 16 was centered on a particular visual shape. Bars in thegraphs of FIGS. 7A and 7B represent the mean percentage tracking timewith error bars denoting the standard deviation. As shown in FIG. 7A,BBD “subjects” with intact reentrant connectors tracked the targets(white bars) significantly more than the distracters (gray bars) foreach target shape, averaging over all approaches (* denote p<0.01 usinga paired sample nonparameter sign test). As shown in FIG. 7B, “subjects”with reentrant connections intact (white bars) tracked targetssignificantly better than “subjects” with “lesions” only during testing(light gray bars), and subjects with lesions during both training andtesting (black bars) (* denote p<0.01 using a RankSum test).

FIG. 7A shows that all “subjects” successfully tracked the fourdifferent targets over 80% of the time. This, despite the fact that thetargets and distracters appeared in the visual field of camera 16 atmany different scales and at many different positions as NOMAD 10explored its environment (invariant object recognition described below).Moreover, NOMAD 10 achieved this process even though because of sharedproperties (e.g. same color or same shape), targets cannot be reliablydistinguished from distracters on the basis of color or shape alone.

To investigate the importance of the presence of reentrant connectionsin the various “subjects” of the brain-based device BBD, certaininter-areal reentrant connections were lesioned at different stages ofthe experimental paradigm with the results shown in FIG. 7B. In onecase, previously trained “subjects” were retested after lesioning. In asecond case, reentrant connections were lesioned in both training andtesting stages. Lesions were applied to a subset of inter-arealexcitatory reentrant connections (see projections marked with an “X” inFIG. 2 and in Table 2), which had the effect of transforming thesimulated nervous system 12 into a “feed-forward” model of visualprocessing. To compensate for the reduction in activity due to theselesions, neuronal unit outputs in areas V2 and V4 were amplified (seeTable 1). FIG. 7B shows that “subjects” with intact reentrantconnections performed significantly better than either lesioned group.The decrease in performance observed in the absence of reentryconnectors indicates that reentrant connections are essential forbehavior, above chance, in the object discrimination task.

Neural Dynamics During Behavior—FIG. 8

During the behavior of NOMAD 10 in its environment, circuits comprisedof synchronously active neuronal groups were distributed throughoutdifferent neural areas in the simulated nervous system 12. Multipleobjects in the environment were distinguishable by the differences inphase between the corresponding active circuits. A snapshot of theneural responses during a typical behavioral run is given in FIG. 8.This snapshot shows NOMAD 10 during an approach to a red diamond targetand a green diamond distracter towards the end of a training session(FIG. 6A). Each pixel in the depicted neural areas V2, V4, IT, C and Srepresents the activity and phase of a single neuronal unit within therespective given neural area. Thus, for example, FIG. 8 shows theresponses for neural areas V2 and V4 specifically their neural sub-areasin color (red, green) and line orientation (vertical, diagonal). Thephase is indicated by the color of each pixel and the activity isindicated by brightness of the pixel (black is no activity; very brightis maximum activity).

FIG. 8 shows two neural circuits which are differentiated by theirdistinct phases and which were elicited respectively by the red diamondand the green diamond stimuli. As shown in the figure, NOMAD 10 has notyet reached the IR beam that triggers the speakers in its environment toemit a tone (see FIG. 6A). The activity of neural area S (the valuesystem) was nonetheless in phase with the activity in neural areas V2and V4 corresponding to the target, and was therefore predictive of thetarget's saliency or value. Area IT has two patterns of activity,indicated by the two different phase colors, which reflect twoperceptual categories. These patterns were brought about by visual inputfrom camera 16 that is generated during the movement of NOMAD 10 in thisenvironment. Finally, neural area C has more activity on the side thatfacilitates orientation of NOMAD 10 towards the target (i.e. the reddiamond).

Dynamics of Neural Responses—FIGS. 9A and 9B

To analyze the dynamics of these neural responses, the phasedistributions of active neuronal units during approaches totarget-distracter pairs in the testing sessions were examined. FIG. 9Ashows the distribution of neuronal phases in various neural areas duringapproaches to a red diamond target in the presence of a red squaredistracter, by an intact “subject” (with reentrant connections). FIG. 9Ashows consistent correlations among phase distributions in neuralsub-areas V4R (red), V4H (horizontal), and V4D (diagonal). The bimodaldistribution in neural sub-area V4R reflects the presence of two redshapes (diamond, square) in the environment of NOMAD 10: one tracecorrelates reliably with neural sub-area V4D (diagonal) and cantherefore be associated with the red diamond, the other correlatesreliably with sub-area V4H (horizontal) and can be associated with thered square (not shown is area V4G which remained inactive during thisperiod). The phases of the active neuronal units in areas S, IT, and Cwere strongly correlated with the red diamond target, as opposed to thered square distracter, reflecting the synaptic changes brought about byprevious conditioning during the testing phase to prefer the reddiamond. The global pattern of network activity thus displayed a biasedphase distribution in favor of the target.

Quantification of Biased Phase Distribution—FIGS. 9A-9B; Table 3

To quantify this bias and assess its generality, the proportion ofneuronal units in areas S, IT, and C associated with the target with theproportion associated with the distracter during the testing. Table 3shows average values of these proportions calculated over all “subjects”and all four target shapes.

TABLE 3 Neuronal composition and average activity of functional circuitscorresponding to target and distracter objects mean firing mean firing %units % units rate of units rate of units responding respondingresponding responding Area to target to distracter to target todistracter S 61.25 (17.78)* 10.34 (4.88)  0.537 (0.089)  0.495 (0.080)IT  4.29 (0.495)*  2.91 (0.366) 0.579 (0.064)* 0.467 (0.015) C 19.04(1.45)*  10.80 (1.93)  0.626 (0.062)* 0.398 (0.032) V4 14.83 (0.833)*11.61 (0.819) 0.829 (0.003)* 0.823 (0.002)A significantly greater proportion of neuronal units were part offunctional circuits associated with targets than in circuits associatedwith distracters. In addition, those neuronal units associated withtargets had significantly higher firing rates than neuronal units incircuits associated with distracters.

The above shows that perceptual categorization and visual objectdiscrimination by NOMAD 10 is enabled by the coherent interaction oflocal and global neuronal circuit processes, as mediated by reentrantconnections, of simulated nervous system 12. Local processes correspondto activity in each neural area, whereas global processes correspond tothe distinct, but distributed functional circuits that emergedthroughout the simulated nervous system 12. These interactions areevident in FIG. 9A, in which activity in each of the local areasstrongly reflects the global bias in favor of the red-diamond target(see also Table 3).

The Influence of Reentry on Neural Dynamics

Lesioning of reentrant connections interfered significantly withinteractions between the local and global processes mentioned above.Even in a very simple network model, removal of reentrant connectionscan prevent the emergence of neural synchrony (see FIGS. 3A-3E). On alarger scale, FIG. 9B shows approaches by the same NOMAD 10 “subject”depicted in FIG. 9A to the same target/distracter pair, followinglesions of inter-areal excitatory reentrant connections. While someindividual areas continued to show peaks in their phase distribution(e.g. neural sub-area V4R), many do not, and the phase correlationsbetween the neural areas are severely diminished. This occurred not onlyamong the various V4 neural areas (FIG. 2), but also among area V4 andareas S, IT, and C. The dynamically formed and globally coherentcircuits, which were clearly evident in the intact “subject”, werealmost entirely absent in the lesioned “subjects”. For example, FIG. 9Bshows that activity in area S no longer correlates uniquely with asingle trace in area V4; instead, it alternates between two distinctstates. The absence of a dominant trace in neural areas IT and C is alsoshown.

Phase correlations between neural areas were significantly higher for“subjects” with intact reentrant connections than for “subjects” ineither lesion group. The overall median rank correlation coefficient was0.36 for the intact “subjects”, 0.21 for the “subjects” with lesionsonly during the test stage, and 0.17 for the “subjects” with lesions inboth the training and test stages. Also, “subjects” with lesions onlyduring testing had significantly higher correlation coefficients than“subjects” with lesions during both training and testing. This reflectsthe contribution of reentrant connections to the formation of globalcircuits during training (FIG. 6A). All of these findings are consistentwith the drop in behavioral performance in the absence of reentrantconnections (see FIG. 7).

Phase Correlations Among Neural Areas—Single “Subject” Conditioned to aRed Diamond Shape

FIGS. 10A-10C illustrate geographically a representative example of thecorrelation of phases among neural areas for a “subject” afterconditioning to prefer red diamond targets. The figures are color coded(dark blue denotes no correlation, dark red denotes high correlation),and each colored area shows the correlation coefficient between the meanphases of a given pair of neural areas. FIG. 10A shows correlationcoefficients when reentrant connections were intact. In agreement withthe data shown in FIG. 9, strong phase correlations were found betweenareas associated with specific target features (V4D and V4R), and amongthese areas and areas S, IT and C. The correlations among neural areasfor the same “subject” with reentrant connections lesioned duringtesting (FIG. 10B) and with reentrant connections lesioned during bothconditioning and testing (FIG. 10C) were both considerably weaker. Asgraphically indicated, the connections between neural areas V1, V2, etc.associated with the target are considerably higher with reentrantconnections intact (FIG. 10A) than in either lesion case (FIGS. 10B and10C).

Invariant Object Recognition—FIGS. 11A and 11B

FIGS. 11A and 11B are graphs illustrating the response of neural valuearea S to target objects in the visual field of NOMAD 10 at differentpositions and at different scales. Average values were calculated forall approaches by NOMAD 10 “subjects” to all target objects, with theerror bars indicating standard errors. FIG. 11A shows average responsesas a function of target position within the visual field (135°). FIG.11B shows average responses as the apparent target size ranged from 8°to 27° of visual angle. The insets in FIGS. 11A and 11B indicate how thesquare target appears in NOMAD 10's field of view at extreme positionsand scales.

Because images of the visual objects varied considerably in size andposition as NOMAD 10 explored its enclosure, successful discriminationrequired invariant object recognition. In order to analyze thiscapacity, the value system, i.e. neural area S, was examined which,after conditioning, responded preferentially to target objects overdistracters due to plasticity in the pathway IT→S. In a typicalapproach, as NOMAD 10 moved from one side of the environment to theother, neural area S responded briskly and in phase with neuronal unitsin areas V2, V4, and IT corresponding to attributes of the target.Calculating average values over all “subjects” and all target shapes, itwas found that area S responded reliably to target images which appearedwithin 120° of the center of the field of view (the range of the visualfield was approximately ±35°) and as the apparent target size rangedfrom 8° to 27° of visual angle. Thus, the object recognition of thebrain-based device BBD of the present invention while autonomouslymoving in its environment was both position and scale invariant.

Value System (Neural Area S) Activity During Conditioning—FIGS. 12A and12B

Neural activity during conditioning for a single “subject”, for neuralareas S, IT, and C during a single approach to a target shape is shownin FIG. 12A, at an early stage (left panels, time steps 750-1165), andin FIG. 12B, at a late stage of conditioning (right panels, time steps6775-7170). Each panel shows the distribution of neuronal unit phases inthe corresponding neural area over time. As in FIGS. 9A and 9B, a grayscale indicates the proportion of neuronal units in each neural area ata particular phase. The solid line at the bottom of each panel indicatestime steps for which the tone from a speaker was present (see FIG. 6A).In the early conditioning training period (left panels) area S isinactive until tone onset, i.e. an audible activity, at which point itbecomes strongly activated in phase with the upper traces in both areasIT and C, which are associated with the target. The lower traces inareas IT and C, corresponding to the distracter, become relativelysuppressed at the same time. Later in conditioning (right panels), areasS, IT and C are in phase with visual system activity corresponding tothe target (lower trace) well before tone onset, and activity associatedwith the distracter is relatively suppressed well before the tone onset.

As a result of value-dependent synaptic plasticity during conditioning(i.e. the plasticity of the synaptic connectors are dependent on value),the visual attributes of target objects became predictive of value. Asshown in FIG. 12A, during early conditioning area S does not becomeactive until the UCS (unconditioned stimulus; i.e. the tone) is present.The UCS also evokes biases in areas IT and C, as shown by the rapidabolition of the initially bimodal phase distributions in these areas.

At a later stage of conditioning, the CS (the conditioned stimulus; i.e.the target visual features) has become associated with value such thatactivity in area S now precedes UCS onset (see FIG. 12B). Area Sresponds to the target stimulus as soon as the stimulus appears in NOMAD10's visual field. Activity in area S then facilitates a bias in areasIT and C, as shown in FIGS. 12A-12B by the appearance of a single phasedistribution peak in each area well before UCS onset. This shift in thetiming of value-related activity, from activity triggered by theauditory UCS in early trials (i.e. auditory input provides value), toactivity triggered by the visual CS in later trials (i.e. visual inputnow provides value), is analogous to the shift in dopaminergic neuralactivity found in the primate ventral tegmental area duringconditioning. Value-dependent synaptic plasticity is also similar to“temporal-difference” learning in that the conditioned stimulus becomespredictive of value.

Computer System and Flow Charts

FIG. 13 is an exemplary illustration of a system in accordance withvarious embodiments of the invention. Although this diagram depictscomponents as logically separate, such depiction is merely forillustrative purposes. It will be apparent to those skilled in the artthat the components portrayed in this figure can be arbitrarily combinedor divided into separate software, firmware and/or hardware components.Furthermore, it will also be apparent to those skilled in the art thatsuch components, regardless of how they are combined or divided, canexecute on the same computing device or can be distributed amongdifferent computing devices connected by one or more networks or othersuitable communication means.

In various embodiments, the components illustrated in FIG. 13 can beimplemented in one or more programming languages (e.g., C, C++, Java™,and other suitable languages). Components can communicate using MessagePassing Interface (MPI) or other suitable communication means, includingbut not limited to shared memory, distributed objects and Simple ObjectAccess Protocol (SOAP). MPI is an industry standard protocol forcommunicating information between computing devices (or nodes). In oneembodiment, the system can be deployed on a multi-processor computerarchitecture such as (but not limited to) a Beowulf cluster. Beowulfclusters are typically comprised of commodity hardware components (e.g.,personal computers running the Linux operating system) connected viaEthernet or some other network. The present disclosure is not limited toany particular type of parallel computing architecture. Many other sucharchitectures are possible and fully within the scope and spirit of thepresent disclosure.

Referring to FIG. 13, master component 1302 can coordinate theactivities of the other components according to commands received fromclient 1304. In one embodiment, the client can be a stand-alone processthat programmatically controls the master according to a script or otherscenario and/or in reaction to client information (e.g., neuralactivity, sensor readings and camera input) received from the master.Client commands can instruct the master to start or stop the brain-baseddevice BBD experiment, save the experiment state on data store 1312,read the experiment state from the data store, set the runningtime/cycles in which the experiment will execute, and set parameters ofthe neural simulators 1310.

In another embodiment, the client can be a user interface that receivesinformation from the master and allows a user to interactively controlthe system. By way of a non-limiting example, a user interface caninclude one or more of the following: 1) a graphical user interface(GUI) (e.g., rendered with Hypertext Markup Language); 2) an ability torespond to sounds and/or voice commands; 3) an ability to respond toinput from a remote control device (e.g., a cellular telephone, a PDA,or other suitable remote control); 4) an ability to respond to gestures(e.g., facial and otherwise); 5) an ability to respond to commands froma process on the same or another computing device; and 6) an ability torespond to input from a computer mouse and/or keyboard. This disclosureis not limited to any particular UI. Those of skill in the art willrecognize that many other user interfaces are possible and fully withinthe scope and spirit of this disclosure.

The neuronal units for each neural area (e.g., V1, V2, V4, IT, C, S,Mic-left, A-left, Mic-right, A-right) are each assigned to a neuralsimulator 1310. Each neural simulator 1310 is responsible forcalculating the activity of the neuronal units that have been assignedto it. A given neural area's neuronal units may be distributed acrossone or more neural simulators 1310. In various embodiments, there can beone neural simulator per Beowulf node. In order to optimize performance,neuronal units can be distributed among neural simulators such that theaverage number of synaptic connections on the neural simulators isapproximately the same. In other embodiments, neuronal units can bedistributed such that the average number of neuronal units per neuralsimulator is approximately the same. Neural simulators periodically orcontinuously exchange the results of calculating the activity of theirneuronal units with other neural simulators and the master. Thisinformation is required so that neuronal units on other neuralsimulators have up-to-date pre-synaptic inputs. The master providesactuator commands to the NOMAD based on the neural activity receivedfrom the neural simulators.

The master periodically receives image data from image grabber 1306 anddistributes it to the neural simulators and to the client. In oneembodiment, the images are taken from the CCD camera 16 mounted on NOMAD10 that sends 320×240 pixel RGB video images, via an RF transmitter, toan ImageNation PXC200 frame grabber. The image is then spatiallyaveraged to produce an 80×60 pixel image. Gabor filters can be used todetect edges of vertical, horizontal, and diagonal (45 and 135 degrees)orientations (as briefly described above). The output of the Gaborfunction is mapped directly onto the neuronal units of the correspondingV1 sub-area. Color filters (red positive center with a green negativesurround, or red negative center with a green positive surround) arealso applied to the image. The outputs of the color filters are mappeddirectly onto the neuronal units of V1-Red and V1-Green. V1 neuronalunits projected retinotopically to neuronal units in neural area V2.

The master component also periodically acquires sensor data from NOMAD10 component 1308 and distributes it to the neural simulators. In oneembodiment, a micro controller (PIC17C756A) onboard the NOMAD 10 samplesinput and status from its sensors and controls an RS-232 communicationbetween the NOMAD base and master. Sensor information can include, inaddition to video and audio information previously described, gripperstate, camera position, infrared detectors, whisker deflection, wheelspeed and direction, odometer count, and microphone input. In oneembodiment, a root mean square (RMS) chip measures the amplitude of themicrophone input signal and a comparator chip produces a square waveformwhich allows frequency to be measured. A micro controller on NOMAD 10periodically calculates the overall microphone amplitude by averagingthe current signal amplitude measurement with the previous threemeasurements. The micro controller calculates the frequency of themicrophone signal at each time point by inverting the average period ofthe last eight square waves. Neural areas Mic-left and Mic-right respondto tones between 2.9 and 3.5 kHz having an amplitude of at least 40% ofthe maximum. The activity of a neuronal unit in neural area Mic-left orMic-right is given by

s _(i) ^(mic)(t+1)=tan h(0.9s _(i) ^(mic)(t)+0.1a _(i) ^(mic)),

where s_(i) ^(mic)(t) is the previous value of a neuronal unit i inMic-left or Mic-right, and a_(i) ^(mic) is the current amplitude of themicrophone output.

FIG. 14 is a flow diagram illustration of neural simulatorinitialization in accordance with various embodiments of the invention.Although this figure depicts functional steps in a particular order forpurposes of illustration, the process is not necessarily limited to anyparticular order or arrangement of steps. One skilled in the art willappreciate that the various steps portrayed in this figure can beomitted, rearranged, performed in parallel, combined and/or adapted invarious ways. In step 1402, it is determined based on command(s) fromthe client 1304 whether or not a saved experiment should be retrievedfrom the data store 1312 or whether a new experiment should be started.If the experiment is to be retrieved from the data store, this isperformed in step 1410. In various embodiments, the experiment state canbe stored as an Extensible Markup Language (XML) document, a plain textfile, or a binary file. Otherwise, in step 1404 neuronal units arecreated according to the parameters given in Table 1. Next, in step 1406synaptic connections are created between the neuronal units according tothe parameters in Table 2. Finally, each neuronal unit is assigned to aneural simulator in step 1408.

FIG. 15 is a flow diagram illustration of the master component inaccordance with various embodiments of the invention. Although thisfigure depicts functional steps in a particular order for purposes ofillustration, the process is not necessarily limited to any particularorder or arrangement of steps. One skilled in the art will appreciatethat the various steps portrayed in this figure can be omitted,rearranged, performed in parallel, combined and/or adapted in variousways.

In step 1502 the master broadcasts image and sensor data that it hasacquired from the image grabber and NOMAD 10 to the neural simulatorsand the client. In step 1504, the master broadcasts any commands it mayhave received to the neural simulators. In step 1506, it is determinedwhether or not the client has directed the master to quit theexperiment. If so, the master ceases the experiment (which may includesaving the state of the experiment to the data store). Otherwise, instep 1508 the updated information is provided to the client which couldserve to update a GUI. In step 1510, neuronal unit activity from theneural simulators is shared among all components (e.g., via MPI). Theneuronal activity can be provided in some form to the client as part ofthe client information. Finally, it is determined whether or not thereare any remaining cycles left in the simulation. If not, the experimentterminates. Otherwise, the master returns to step 1502.

FIG. 16 is a flow diagram illustration of a neural simulator inaccordance to various embodiments of the invention. Although this figuredepicts functional steps in a particular order for purposes ofillustration, the process is not necessarily limited to any particularorder or arrangement of steps. One skilled in the art will appreciatethat the various steps portrayed in this figure can be omitted,rearranged, performed in parallel, combined and/or adapted in variousways.

In step 1602, the neural simulator accepts image and sensor data that isbroadcast by the master. In step 1604, client commands broadcast by themaster are accepted. In step 1606, it is determined whether or not theclient has directed the master to quit the experiment. If so, the neuralsimulator completes its execution. Otherwise, in step 1608 the value ofthe neuronal units assigned the neural simulator are calculated. In step1610, the strengths of plastic connections are calculated. Localneuronal unit activity is shared in step 1612 with other neuralsimulators and the master. In addition, neuronal activity from otherneural simulators is acquired and used to refresh local values. Finally,it is determined in step 1614 whether or not there are any remainingcycles left in the simulation. If not, the experiment terminates.Otherwise, the neural simulator returns to step 1602.

Various embodiments may be implemented using a conventional generalpurpose or a specialized digital computer or microprocessor(s)programmed according to the teachings of the present disclosure, as willbe apparent to those skilled in the computer art. Appropriate softwarecoding can readily be prepared by skilled programmers based on theteachings of the present disclosure, as will be apparent to thoseskilled in the software art. The invention may also be implemented bythe preparation of integrated circuits or by interconnecting anappropriate network of conventional component circuits, as will bereadily apparent to those skilled in the art.

Various embodiments include a computer program product which is astorage medium (media) having instructions stored thereon/in which canbe used to program a general purpose or specialized computingprocessor/device to perform any of the features presented herein. Thestorage medium can include, but is not limited to, one or more of thefollowing: any type of physical media including floppy disks, opticaldiscs, DVDs, CD-ROMs, microdrives, magneto-optical disks, ROMs, RAMs,EPROMs, EEPROMs, DRAMs, VRAMs, flash memory devices, magnetic or opticalcards, nanosystems (including molecular memory ICs); and any type ofmedia or device suitable for storing instructions and/or data. Variousembodiments include a computer program product that can be transmittedover one or more public and/or private networks wherein the transmissionincludes instructions which can be used to program a computing device toperform any of the features presented herein.

Stored one or more of the computer readable medium (media), the presentdisclosure includes software for controlling both the hardware of thegeneral purpose/specialized computer or microprocessor, and for enablingthe computer or microprocessor to interact with a human user or othermechanism utilizing the results of the present invention. Such softwaremay include, but is not limited to, device drivers, operating systems,execution environments/containers, and applications.

Summary

A brain-based device (BBD), including NOMAD 10 controlled by a simulatednervous system 12 has been discussed, which bound the visual attributesof distinct stimuli. Binding in the brain-based device BBD occurred as aresult of multilevel interactions involving a reentrant neuroanatomy(FIG. 2, Table 1 and Table 2), the dynamic synchronization of neuronalgroups, and the correlations generated by synaptic plasticity andautonomous behavior of NOMAD 10 moving in its environment. Specifically,during approaches to visual objects the formation of synchronouslyactive neuronal circuits occurred for each object in the visual field ofNOMAD 10. These circuits, which were enabled by reentrant connectionswithin and among neural areas V1, V2, etc., gave rise to motor areaactivity which in turn evoked discriminatory behavior of NOMAD 10. Thisprovides insight into the complex, dynamic interactions between brain,body, and behavior that underlie effective visual object recognition.

The brain-based device BBD of the present invention has innatelyspecified behavior (i.e. tracking towards auditory or visual stimuli)and innately specified value or salience for certain environmentalsignals (e.g. positive value of sound). The BBD learned autonomously toassociate the value of the sound with the attributes of the visualstimulus closest to the sound source, and, it successfully orientedtowards the target object based on visual attributes alone (see FIG.7A).

The physical embodiment of the brain-based device was important forincorporating many of the challenging aspects of this objectdiscrimination task, such as variations in the position, scale andluminosity of visual images, sound reflections, and slippages duringmovement. Reliance on elaborate computer simulations risks introducing apriori biases in the form of implicit instructions governinginteractions between an agent and its environment. By the use of areal-world environment, however, not only is the risk of introducingsuch biases avoided, but also the need for the construction of a highlycomplex simulated environment is eliminated.

The simulated nervous system 12 of the present invention containscortical areas analogous to the ventral occipito-temporal stream of thevisual system (areas V2, V4, and IT), the motor system (area C), as wellas reward or value systems (area S) analogous to diffuse ascendingneuromodulatory systems. None of these specialized areas, however, norpreferential directions of information flow (e.g. “top-down” or“bottom-up”), are by themselves sufficient for binding the features ofvisual objects. Rather, visual binding in the brain-based device BBD isachieved through the interaction of local processes (i.e. activity ineach simulated neural area), and global processes (i.e. emergentfunctional circuits characterized by synchronous activity distributedthroughout the simulated nervous system 12). Reentrant connections amongdistributed neural areas V1, V2, etc. were found to be essential for theformation of these circuits (see FIGS. 9, 10, and 12) and for successfulperformance in a task requiring discrimination between multiple objectswith shared features (see FIG. 7). The brain-based device BBD of thepresent invention achieved reliable discriminations in the visual field,which resulted from self-generated or autonomous movement in a richreal-world environment (see FIG. 11).

The state of each neuronal unit in the simulated nervous system 12 hasbeen described by both a firing rate variable and a phase variable,where post-synaptic phase tends to be correlated with the phase of themost strongly active pre-synaptic inputs. This modeling strategyprovided the temporal precision needed to represent neural synchrony,without incurring the computational costs associated with modeling ofthe spiking activity of individual neurons. While representation ofprecise spike timing is necessary for modeling certain neuronalinteractions, the disclosed model suggests that for the purposes ofillustrating the mechanism for visual binding, such detail is notrequired. It is also important to emphasize that phase in the describedmodel is not intended as a reflection of possible underlying oscillatoryactivity, specifically, it should not be taken to imply that regularbrain oscillations at specific frequencies are an essential component ofthe neural mechanisms of binding.

Although local regions in the simulated nervous system 12 had segregatedfunctions based on their input and connectivity, object recognition andobject discriminative behavior was an emergent property of the wholesystem, not of any individual area. The neural responses of thebrain-based device BBD during an orienting movement toward a targetshowed this global property in terms of synchronized activity among adynamic set of neuronal units in different neural areas (see FIGS. 8 and9A). The simultaneous viewing of two objects clearly evoked two distinctsets of circuits that were distributed throughout the simulated nervoussystem 12 and distinguished by differences in the relative timing oftheir activity. When the reentrant connections between neural areas V1,V2, etc. were removed via simulated lesions, coherent interactions amongthese neural areas were disrupted (see FIGS. 9B, 10B, and 10C) resultingin failures in both object perceptual categorization and objectdiscriminative behavior (see FIG. 7B).

Both experience and value shape the global properties of the simulatednervous system 12. This is clearly shown in FIGS. 12A and 12B where,during early training, area S showed no activity, and area C showed nobias toward the target object until the onset of the auditory cue, i.e.value. Late in the training, area S became active well before theauditory cue onset as a result of the value-dependent plasticconnections from area IT to area S, i.e. value of visual stimuli.Activity in area S therefore became predictive of the unconditionedstimuli (i.e. the auditory tone). Value-dependent plastic connectionsfrom area IT to area C and excitatory connections from area S to area Censured that this shift in the timing of value-related activity resultedin a bias in the activity of area C which favored movement toward thetarget in preference to the distracter. This emphasizes the role ofvalue systems in modifying the efficacy of distributed neuralconnections to assure adaptive behavior. Successful performance in theobject discrimination task amongst objects in a visual field requiredthe complementary action of neural synchrony and experience-dependentchanges in neuronal firing rates (see Table 3). Neuronal synchrony,which was indicated by groups of neuronal units sharing a similar phase,was necessary for the formation of multiple global circuitscorresponding to each object in view. At the same time, the activity ofthe neuronal units within these circuits influenced activity levels inareas V4, IT, and C causing NOMAD 10 to favor the target object overdistracters. These observations suggest that mean firing rate “codes”and synchrony-based “codes” need not be considered as mutually exclusiveexplanations of neuronal function.

A prediction of the described model, in which neuronal units representthe activity of small groups of neurons, is that neural synchrony at thegroup level, rather than zero phase lag among individual neurons, may besufficient for sensory binding. Although some single-unit recordingstudies have shown that neurons activated by attended stimuli are moresynchronized than neurons activated by unattended stimuli, synchronousactivity among single units has been difficult to detect in tasksrequiring binding. Also, micro-electrode recordings from primateprefrontal cortex have shown higher levels of correlated firing amonglocal, inhibitory neurons than among excitatory, long-range pyramidalneurons. On the other hand, neuromagnetic recordings of human subjectsduring binocular rivalry have shown an increase in the intra- andinter-hemispheric coherence of signals associated with a perceptuallydominant stimulus, as compared to a stimulus which is not consciouslyperceived. However, neuromagnetic signals do not reflect reentrantrelations between single neurons; rather, they represent averages acrosslarge neuronal populations. This is therefore consistent with the modelof the present invention described above in suggesting that synchronycan operate at a neuronal group level as well as at the single neuronlevel.

Higher brain function depends on the cooperative activity of the entirenervous system, reflecting its morphology, its dynamics, and itsinteractions with the body and the environment. In accord withtheoretical views emphasizing the importance of binding throughsynchrony the brain-based device BBD of the present invention shows thatvisual binding and object discrimination can arise as a result of theconstraints reentry and behavior impose on interactions between localprocesses (activity in particular neural areas) and global processes(synchronously active and broadly distributed neural circuits). Thisinteraction between these processes was essential, and neitherspecialized areas nor deterministic preferential directions ofinformation flow were sufficient alone to achieve visual binding.

The foregoing description of the preferred embodiments of the presentinvention has been provided for purposes of illustration anddescription. It is not intended to be exhaustive or to limit theinvention to the precise forms disclosed. Many modifications andvariations will be apparent to the practitioner skilled in the art.Embodiments were chosen and described in order to best explain theprinciples of the invention and its practical application, therebyenabling others skilled in the art to understand the invention, thevarious embodiments and with various modifications that are suited tothe particular use contemplated. It is intended that the scope of theinvention be defined by the following claims and their equivalents.

1-13. (canceled)
 14. A mobile brain-based device (BBD) for behaving in areal-world environment to integrate a visual scene, comprising: a. amobile adaptive device having i. a visual input sensor for receivingvisual information; and ii. an effector for enabling movement of saidmobile adaptive device; b. a computer-based simulated nervous systemmodeling the regional and functional neuro-anatomy of the corticalregions of a human brain for visually recognizing and discriminatingbetween different objects within the visual scene, said computer-basedsimulated nervous system including i. a first neural area forming avisual system and responsive to visual input from said visual inputsensor for producing visual stimuli, said first neural areacorresponding to the ventral cortical pathway of the brain for producingvisual stimuli; ii. a second neural area, analogous to an ascendingneuromodulatory system, responsive to a real-world salient eventexperienced by the mobile brain-based device while being mobile in itsreal-world environment, for producing value stimuli; and iii. a thirdneural area, corresponding to the superior colliculus area of the brainand responsive to said visual and value stimuli, for controlling saideffector to orient said mobile adaptive device towards the visual inputinformation to said mobile adaptive device; c. wherein visualrecognition and discriminating between different objects is achievableduring real-world mobility of said mobile adaptive device throughreentrant connectivity of neuronal units within each of said first,second and third neural areas, through reentrant connectivity betweensaid first, second and third neural areas, and through the interactionof local processes, which are activities within each of said first,second and third neural areas, and global processes which createfunctional neural circuits formed during the real-world operation andhaving synchronous activity between said first, second and third neuralareas; d. wherein connectivity between said first neural area and saidsecond neural area are value-dependent synaptic plastic connections; e.wherein connectivity from said first neural area to said third neuralarea are value-dependent synaptic plastic connections; and f. whereinconnectivity from said second neural area to said third neural area areexcitatory synaptic plastic connections.
 15. A mobile brain-based deviceaccording to claim 14, wherein each said first, second and third neuralareas has neuronal units, and wherein said neuronal units in each saidarea have relative neuronal activity whose timing is represented by afiring rate variable and the relative timing of which is represented bya phase variable, in which similar firing phases of neuronal units insaid areas reflect synchronous activity.
 16. A mobile brain-based deviceaccording to claim 14, wherein said value stimuli modify the strength ofthe synaptic plastic connections between said first, second and thirdneural areas to provide for the adaptive behavior of the mobilebrain-based device in a real-world environment.
 17. A mobile brain-baseddevice according to claim 14, wherein said first neural area correspondsto said vertical cortical pathway having neural areas V1, V2, V4 and ITbeing coupled in a pathway V1→V2→V4→IT.
 18. A mobile brain-based deviceaccording to claim 14, wherein each of said first, second and thirdneural areas includes neuronal units, in which said neuronal units haveexcitatory synaptic connections amongst themselves, and each of saidexcitatory synaptic connections are voltage-dependent.
 19. A mobilebrain-based device according to claim 18, wherein said first, second andthird neural areas have reentrant excitatory connections between saidareas, and all said reentrant excitatory connections arevoltage-dependent.